/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
package org.apache.solr.ltr.feature.extraction;

import java.io.IOException;
import java.util.Map;
import org.apache.lucene.search.DisiPriorityQueue;
import org.apache.lucene.search.DisiWrapper;
import org.apache.solr.ltr.LTRScoringQuery;
import org.apache.solr.ltr.feature.Feature;
import org.apache.solr.ltr.model.LTRScoringModel;
import org.apache.solr.ltr.scoring.FeatureTraversalScorer;
import org.apache.solr.request.SolrQueryRequest;

/** The class used to extract more than one feature for LTR feature logging. */
public class MultiFeaturesExtractor extends FeatureExtractor {
  DisiPriorityQueue subScorers;

  public MultiFeaturesExtractor(
      FeatureTraversalScorer multiFeaturesScorer,
      SolrQueryRequest request,
      Feature.FeatureWeight[] extractedFeatureWeights,
      LTRScoringQuery.FeatureInfo[] allFeaturesInStore,
      LTRScoringModel ltrScoringModel,
      Map<String, String[]> efi,
      DisiPriorityQueue subScorers) {
    super(
        multiFeaturesScorer,
        request,
        extractedFeatureWeights,
        allFeaturesInStore,
        ltrScoringModel,
        efi);
    this.subScorers = subScorers;
  }

  @Override
  protected float[] extractFeatureVector() throws IOException {
    final DisiWrapper topList = subScorers.topList();
    float[] featureVector = initFeatureVector(allFeaturesInStore);
    for (DisiWrapper w = topList; w != null; w = w.next) {
      final Feature.FeatureWeight.FeatureScorer subScorer =
          (Feature.FeatureWeight.FeatureScorer) w.scorer;
      Feature.FeatureWeight feature = subScorer.getWeight();
      final int featureId = feature.getIndex();
      float featureValue = subScorer.score();
      featureVector[featureId] = featureValue;
    }
    return featureVector;
  }
}
